import torch
from torch import nn

import os
class VGGBlock(nn.Module):
    def __init__(self, in_channels, middle_channels, out_channels):
        super().__init__()
        self.first = nn.Sequential(
            nn.Conv2d(in_channels, middle_channels, 3, padding=1, bias=False),
            nn.BatchNorm2d(middle_channels),
            nn.ReLU(inplace=True)
        )
        self.second = nn.Sequential(
            nn.Conv2d(middle_channels, out_channels, 3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        out = self.first(x)
        out = self.second(out)

        return out


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()

        self.residual_function = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels * BasicBlock.expansion, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels * BasicBlock.expansion)
        )

        self.shortcut = nn.Sequential()

        if stride != 1 or in_channels != BasicBlock.expansion * out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels * BasicBlock.expansion)
            )

    def forward(self, x):
        return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))


class BottleNeck(nn.Module):
    expansion = 4

    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()
        self.residual_function = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, stride=stride, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels * BottleNeck.expansion, kernel_size=1, bias=False),
            nn.BatchNorm2d(out_channels * BottleNeck.expansion),
        )

        self.shortcut = nn.Sequential()

        if stride != 1 or in_channels != out_channels * BottleNeck.expansion:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels * BottleNeck.expansion, stride=stride, kernel_size=1, bias=False),
                nn.BatchNorm2d(out_channels * BottleNeck.expansion)
            )

    def forward(self, x):
        return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))


class NestedUResnet(nn.Module):
    def __init__(self, block, layers, num_classes, input_channels=3, deep_supervision=False):
        super().__init__()

        nb_filter = [64, 128, 256, 512, 1024]
        self.in_channels = nb_filter[0]
        self.relu = nn.ReLU()
        self.deep_supervision = deep_supervision

        self.pool = nn.MaxPool2d(2, 2)
        self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)

        self.conv0_0 = VGGBlock(input_channels, nb_filter[0], nb_filter[0])
        self.conv1_0 = self._make_layer(block, nb_filter[1], layers[0], 1)
        self.conv2_0 = self._make_layer(block, nb_filter[2], layers[1], 1)
        self.conv3_0 = self._make_layer(block, nb_filter[3], layers[2], 1)
        self.conv4_0 = self._make_layer(block, nb_filter[4], layers[3], 1)

        self.conv0_1 = VGGBlock(nb_filter[0] + nb_filter[1] * block.expansion, nb_filter[0], nb_filter[0])
        self.conv1_1 = VGGBlock((nb_filter[1] + nb_filter[2]) * block.expansion, nb_filter[1],
                                nb_filter[1] * block.expansion)
        self.conv2_1 = VGGBlock((nb_filter[2] + nb_filter[3]) * block.expansion, nb_filter[2],
                                nb_filter[2] * block.expansion)
        self.conv3_1 = VGGBlock((nb_filter[3] + nb_filter[4]) * block.expansion, nb_filter[3],
                                nb_filter[3] * block.expansion)

        self.conv0_2 = VGGBlock(nb_filter[0] * 2 + nb_filter[1] * block.expansion, nb_filter[0], nb_filter[0])
        self.conv1_2 = VGGBlock((nb_filter[1] * 2 + nb_filter[2]) * block.expansion, nb_filter[1],
                                nb_filter[1] * block.expansion)
        self.conv2_2 = VGGBlock((nb_filter[2] * 2 + nb_filter[3]) * block.expansion, nb_filter[2],
                                nb_filter[2] * block.expansion)

        self.conv0_3 = VGGBlock(nb_filter[0] * 3 + nb_filter[1] * block.expansion, nb_filter[0], nb_filter[0])
        self.conv1_3 = VGGBlock((nb_filter[1] * 3 + nb_filter[2]) * block.expansion, nb_filter[1],
                                nb_filter[1] * block.expansion)

        self.conv0_4 = VGGBlock(nb_filter[0] * 4 + nb_filter[1] * block.expansion, nb_filter[0], nb_filter[0])

        if self.deep_supervision:
            self.final1 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)
            self.final2 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)
            self.final3 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)
            self.final4 = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)
        else:
            self.final = nn.Conv2d(nb_filter[0], num_classes, kernel_size=1)
    def loadIFExist(self, model_path):
        model_list = os.listdir('./model_result')

        model_pth = os.path.basename(model_path)

        if model_pth in model_list:
            self.load_state_dict(torch.load(model_path))
            print("the latest model has been load")
    def _make_layer(self, block, middle_channels, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_channels, middle_channels, stride))
            self.in_channels = middle_channels * block.expansion
        return nn.Sequential(*layers)

    def forward(self, input):
        x0_0 = self.conv0_0(input)
        x1_0 = self.conv1_0(self.pool(x0_0))
        x0_1 = self.conv0_1(torch.cat([x0_0, self.up(x1_0)], 1))

        x2_0 = self.conv2_0(self.pool(x1_0))
        x1_1 = self.conv1_1(torch.cat([x1_0, self.up(x2_0)], 1))
        x0_2 = self.conv0_2(torch.cat([x0_0, x0_1, self.up(x1_1)], 1))

        x3_0 = self.conv3_0(self.pool(x2_0))
        x2_1 = self.conv2_1(torch.cat([x2_0, self.up(x3_0)], 1))
        x1_2 = self.conv1_2(torch.cat([x1_0, x1_1, self.up(x2_1)], 1))
        x0_3 = self.conv0_3(torch.cat([x0_0, x0_1, x0_2, self.up(x1_2)], 1))
        x4_0 = self.conv4_0(self.pool(x3_0))
        x3_1 = self.conv3_1(torch.cat([x3_0, self.up(x4_0)], 1))

        x2_2 = self.conv2_2(torch.cat([x2_0, x2_1, self.up(x3_1)], 1))
        x1_3 = self.conv1_3(torch.cat([x1_0, x1_1, x1_2, self.up(x2_2)], 1))
        x0_4 = self.conv0_4(torch.cat([x0_0, x0_1, x0_2, x0_3, self.up(x1_3)], 1))

        if self.deep_supervision:
            output1 = self.final1(x0_1)
            output2 = self.final2(x0_2)
            output3 = self.final3(x0_3)
            output4 = self.final4(x0_4)
            return [output1, output2, output3, output4]

        else:
            output = self.final(x0_4)
            return output



if __name__ == "__main__":
    from torchsummary import summary

    net = NestedUResnet(block=BasicBlock,layers=[3,4,6,3],num_classes=21)
    net.cuda()
    summary(net, (3, 128, 128))